Multi-energy micro-grid day-ahead optimization scheduling method based on mixed state machine modeling

Abstract

A multi-energy micro-grid day-ahead optimization scheduling method based on mixed state machine modeling comprises the following steps: establishing a state transition model of a multi-energy micro-grid for describing dynamic characteristics of the multi-energy micro-grid in different states; establishing a multi-energy micro-grid day-ahead robust optimization scheduling model considering load and renewable energy output fluctuation; solving a day-ahead robust optimization scheduling initial state transition path of the multi-energy micro-grid; and based on an improved genetic algorithm, obtaining a day-ahead robust optimization scheduling optimal state transition path of the multi-energy micro-grid. According to the day-ahead optimal scheduling method for the multi-energy micro-grid, the multi-energy micro-grid state transition model based on the mixed state machine is established and is applied to day-ahead optimal scheduling of the multi-energy micro-grid, so that the running state and the state transition process of the multi-energy micro-grid can be accurately described; and the solution time complexity is relatively low while the optimality of the solution is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid optimal scheduling, and particularly to a day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling. Background Art

[0002] A multi-energy microgrid is an integrated energy system that integrates various energy forms such as electricity, heat, and natural gas, and can achieve efficient utilization and collaborative optimization of energy. Different from a power system containing a single energy source, a multi-energy microgrid includes multiple types of energy, various devices, and energy networks. Among them, the dynamic characteristics of various energy flows such as electricity, gas, and heat are different, and there are also significant differences in the device control characteristics, network characteristics, and the response process of the load to scheduling instructions in each energy subsystem. Coupled with the inaccuracy of renewable energy and load forecasting, it brings great challenges to the optimal scheduling of the multi-energy microgrid. Essentially, the optimal scheduling of a multi-energy microgrid is a complex problem involving multi-energy coupling (electricity, gas, cold, heat), multi-time scales (hourly, minute-level, second-level), and multi-operation states. In the past, single-energy regulation technologies were difficult to adapt to the new integrated energy model, and there was an urgent need to develop an optimal scheduling method suitable for multi-energy microgrids.

[0003] Although domestic and foreign scholars have made many progresses in the day-ahead optimal scheduling of multi-energy microgrids, the existing day-ahead optimal scheduling models established for multi-energy microgrids mainly describe the multi-energy microgrid from the input-output relationship, and cannot describe the operating state of the multi-energy microgrid and the evolution process of the operating state, which is precisely the key to analyzing and improving the efficiency of the multi-energy microgrid. During the day-ahead optimal scheduling process of a multi-energy microgrid, the operating states of devices are variable and include discrete and continuous dynamic characteristics, which makes the operating state and scheduling decision of the multi-energy microgrid highly complex and dynamic. If the operating state of the multi-energy microgrid and the transition process of the operating state can be described, the accuracy and flexibility of the day-ahead optimal scheduling of the multi-energy microgrid can be improved. However, this is rarely considered in the existing multi-energy microgrid modeling research. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling. By establishing a state transition model of the multi-energy microgrid based on a hybrid state machine and applying it to the day-ahead optimal scheduling of the multi-energy microgrid, the operating state of the multi-energy microgrid and its state transition process can be accurately described, and at the same time, it has a low solution time complexity while ensuring the optimality of the solution.

[0005] The technical solution adopted by the present invention is as follows:

[0006] A day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling includes the following steps:

[0007] Step 1: Establish a state transition model for the multi - energy micro - grid to describe the dynamic characteristics of the multi - energy micro - grid in different states; Step 2: Establish a day - ahead robust optimal scheduling model for the multi - energy micro - grid considering the fluctuations of load and renewable energy output;

[0008] Step 3: Obtain the initial state transition path of the day - ahead robust optimal scheduling of the multi - energy micro - grid;

[0009] Step 4: Based on the improved genetic algorithm, obtain the optimal state transition path of the day - ahead robust optimal scheduling of the multi - energy micro - grid. In the above Step 1, establishing the state transition model of the multi - energy micro - grid includes the following steps:

[0010] S1.1: Divide the multi - energy micro - grid into multiple optimizable adjustment states, and each optimizable adjustment state is composed of the operating states of the schedulable units in the multi - energy micro - grid;

[0011] S1.2: Select the energy storage states of electricity storage, gas storage and heat storage in the multi - energy micro - grid as state variables, select the purchased and sold electric power and purchased gas volume as control variables, and establish the state - space equation of the multi - energy micro - grid as follows:

[0012]

[0013] In formula (1): A, B, D and E respectively represent the state matrix, control matrix, coupling matrix and disturbance matrix of the multi - energy micro - grid; x(t), u(t), i(t) and d(t) respectively represent the state variable, control variable, coupling variable and disturbance variable of the multi - energy micro - grid; x(t + 1) represents the state variable at time t+1.

[0014] Among them, each variable is represented as follows.

[0015]

[0016] In formula (2): SOC(t), SOG(t) and SOH(t) respectively represent the stored energy of the electricity storage, gas storage and heat storage units; η bs , η gs and η ts respectively represent the energy conversion efficiency coefficients of the electricity storage, gas storage and heat storage units; v b (t) and P gb (t) respectively represent the power purchase state and purchased electric power of the multi - energy micro - grid, v s (t) and P gs (t) respectively represent the power sale state and sold electric power of the multi - energy micro - grid; P sb (t) represents the purchased gas power of the multi - energy micro - grid; V MT (t) represents the natural gas volume consumed by the gas turbine, P P2G (t) represents the electric power consumed by the power - to - gas equipment, VFC (t) represents the amount of natural gas consumed by the fuel cell, V GB (t) represents the amount of natural gas consumed by the gas boiler, P EC (t) represents the amount of electricity consumed by the electric chiller, Q AC (t) represents the heat absorbed by the absorption chiller; P PV (t), P WT (t), P L,E (t), G L,G (t) and Q L,H (t) respectively represent the photovoltaic power generation, wind power output, electrical load power, gas load, and heat load; T represents the symbol for matrix transpose operation.

[0017] Among them, the coefficient matrix is expressed as follows:

[0018]

[0019] In the above formula: η gt , η loss and H ng respectively represent the electrical conversion efficiency, heat loss rate, and heat conversion efficiency of the gas turbine; η FC , η GB and μ P2G respectively represent the conversion efficiencies of the fuel cell, gas boiler, and power-to-gas equipment; L HV represents the lower calorific value of natural gas.

[0020] S1.3: Establish a state transition model for the multi-energy microgrid based on the hybrid state machine, and the expression of the hybrid state machine is as follows.

[0021] H = {S, X, F, G} (6);

[0022] In the formula: S represents the set of discrete states, X represents the set of continuous state variables, F represents the continuous dynamic equation function, and G represents the state transition condition.

[0023] For the multi-energy microgrid, the set of discrete states is as follows.

[0024] S = {s1, s2, s3,... s n} (7);

[0025] In the formula: s i represents an operating state of the multi-energy microgrid. For the continuous state variable X in the multi-energy microgrid, the stored energies of the electricity storage, gas storage, and heat storage units are selected as the continuous state variables, as shown in Equation (2). Regarding the continuous dynamic equation function F, see the state space equation (1) of the established multi-energy microgrid. Then, the state transition model of the multi-energy microgrid can be established as follows:

[0026]

[0027] The above equation represents the state - space equation of the multi - energy micro - grid under different operating states. The conversion between different states is triggered by the state - transfer condition G.

[0028] In step 2, the optimization objective F in the day - ahead robust optimal scheduling model of the multi - energy micro - grid includes the operating cost F1, the system carbon - emission cost F2, and the load power - shortage rate F3, as follows:

[0029] F = min[F1 + F2 + F3] (9);

[0030] Where:

[0031]

[0032] In equation (10): c e (t) and c s (t) represent the electricity purchase price and the electricity sale price respectively; c g (t) represents the gas purchase price, M represents the number of devices in the multi - energy micro - grid; k o,m represents the unit operation and maintenance cost of device m; P m (t) represents the output of device m at time t. θ represents the treatment cost per unit of CO2 emission; γ e , γ g and γ gt are the equivalent carbon - emission coefficients for electricity purchase, gas purchase, and gas - turbine operation in the multi - energy micro - grid respectively; P gt (t) is the output of the gas turbine at time t; P load is the load power of the multi - energy micro - grid; P loss is the load power missing in the multi - energy micro - grid; △t represents the scheduling time interval.

[0033] The constraint conditions in the day - ahead robust optimal scheduling model of the multi - energy micro - grid include power - balance constraints, device - unit operation constraints, energy - storage - unit operation constraints, and energy - purchase - power constraints;

[0034] ①. Power - balance constraints:

[0035] a: Electric - power balance:

[0036] P PV (t)+P WT (t)+P gt (t)+P gb (t)+P FC (t)+P BS,d (t)=P EC (t)+P gs (t)+P BS,c (t)+PL,E (t) + P P2G (t) (11);

[0037] In Equation (11): On the left side of the equation, P FC (t) and P BS,d (t) represent the output power of the fuel cell and the discharge power of the electricity storage unit at time t respectively; on the right side of the equation, P BS,c (t) and P P2G (t) represent the charging power of the electricity storage unit and the electric power consumed by the power - to - gas device at time t respectively; P PV (t) represents the photovoltaic output power, P WT (t) represents the wind power output, P gt (t) represents the gas turbine output power, P gb (t) represents the purchased power, P EC (t) represents the rated electric power consumed by the electric chiller, P gs (t) represents the sold power, P L,E (t) represents the electric load power.

[0038] b: Thermal power balance:

[0039] Q gb (t)+Q hb (t)+Q TS,d (t) = Q TS,c (t)+Q AC (t)+Q h (t) (12);

[0040] In Equation (12): On the left side of the equation, Q gb (t), Q hb (t) and Q TS,d (t) represent the heat production of the gas boiler, the heat production of the waste heat recovery boiler and the heat released by the heat storage unit at time t respectively; on the right side of the equation, Q TS,c (t) and Q AC (t) represent the heat absorbed by the heat storage unit and the heat consumed by the absorption chiller at time t respectively; Q h (t) represents the heat load power.

[0041] c: Cooling power balance:

[0042] C EC (t)+C AC (t)+C CS,d (t) = C c (t)+C CS,c (t)(13);

[0043] In Equation (13): On the left side of the equation, C EC (t), CAC (t) and C CS,d (t) represent the cooling capacity of the electric chiller, the cooling capacity of the absorption chiller, and the cold quantity released by the cold storage device at time t respectively; on the right side of the equation, C CS,c (t) and C c (t) represent the cold quantity absorbed by the cold storage device and the cold load power at time t.

[0044] d: Gas power balance:

[0045] G gas (t) + G P2G (t) + G GS,d (t) = G L,G (t) + V FC (t) + V MT (t) + V GB (t) + G GS,c (t) (14);

[0046] In equation (14): On the left side of the equation, G P2G (t) and G GS,d (t) represent the gas power output by the power - to - gas device and the natural gas quantity released by the gas storage device at time t respectively; on the right side of the equation, G GS,c (t) represents the natural gas quantity stored in the gas storage device at time t; G gas (t) represents the gas purchase power, G L,G (t) represents the gas load power, V FC (t) represents the natural gas quantity consumed by the fuel cell, V MT (t) represents the natural gas quantity consumed by the gas turbine, V GB (t) represents the natural gas quantity consumed by the gas boiler.

[0047] ②. Equipment unit operation constraints:

[0048]

[0049] In equation (15): η rec represents the recovery efficiency of the waste heat boiler, k ec and k ac represent the refrigeration efficiencies of the electric chiller and the absorption chiller respectively.

[0050] ③. Energy storage unit operation constraints:

[0051]

[0052] In equation (16): x is the type of energy storage; and are the energy states before and after energy charge and discharge respectively; η x,c and η x,drespectively represent the charging efficiency and discharging efficiency; and are the charging and discharging powers at time t, respectively; bs, ts, cs, and gs represent the electrical energy storage, thermal energy storage, cold energy storage, and gas energy storage devices, respectively.

[0053]

[0054] In Equation (17): E x,max and E x,min are the upper and lower limit values of the energy storage device, respectively, P x,c,max and P x,d,max are the maximum values of the charging and discharging energies, respectively, v x,c and v x,d are the 0-1 state variables of the charging and discharging energies, respectively; represents the stored energy of the energy storage device at the initial moment of the scheduling period, represents the stored energy of the energy storage device at the end moment of the scheduling period.

[0055] ④. Purchase energy power constraint:

[0056]

[0057] In Equation (18): P gb,max and P gs,max represent the maximum power of purchasing electricity and selling electricity, respectively, G gas,max represents the maximum power of purchasing gas. Considering that the prediction errors of the renewable energy and load prediction powers are relatively large in the day-ahead optimal scheduling stage, the following equations are used to describe the prediction deviations of the renewable energy and load powers, as shown below:

[0058]

[0059] In Equation (19): β and represent the actual value and predicted value of the renewable energy and load powers, respectively; △β is the fluctuation range of β; β ud and β ld are the upper and lower limits of △β, respectively; ξ ud and ξ ld are the proportional deviations of β relative to △β, respectively.

[0060] A day-ahead robust optimal scheduling model considering the fluctuations of renewable energy and load output is established as follows.

[0061]

[0062] In Equation (20): x and β are the decision variable and uncertain variable, respectively; X and W are their corresponding sets; in order to solve this model, the dual variables α i , β i1, β i2 and Γ i ;

[0063] Transform the above model formula (20) into the solution of a deterministic optimization problem under the worst - case uncertainty scenario as follows:

[0064]

[0065] In formula (21): x ∈ {e, h, c, g} represents the decision variable, where e, h, c, and g respectively represent the symbols for describing electricity, heat, cold, and gas. represents the conservative parameter of the uncertain variable related to electricity, heat, cold, and gas; P PV,0 (t) represents the benchmark value of the photovoltaic output fluctuation range, P WT,0 (t) represents the benchmark value of the wind power output fluctuation range, P L,E0 represents the benchmark value of the electric load power fluctuation range, α e (t) represents the introduced dual variable related to the electric system, Γ e (t) represents the conservative parameter of the uncertain variable related to the electric system, β e1 (t) represents the lower - bound dual variable of the uncertain variable fluctuation range related to the electric system, β e2 (t) represents the upper - bound dual variable of the uncertain variable fluctuation range related to the electric system, β pv1 (t) represents the dual variable of the lower limit of the photovoltaic output fluctuation range, β pv2 (t) represents the dual variable of the upper limit of the photovoltaic output fluctuation range, β wt1 (t) represents the dual variable of the lower limit of the wind power output fluctuation range, β wt2 (t) represents the dual variable of the upper limit of the wind power output fluctuation range, Q TS,d (t) represents the heat release power of the thermal energy storage device, Q TS,c (t) represents the heat charging power of the thermal energy storage device, Q ac (t) represents the heat power consumed by the absorption chiller, Q h,0 (t) represents the benchmark value of the heat load power fluctuation range, α h (t) represents the introduced dual variable related to the thermal system, Γ h (t) represents the conservative parameter of the uncertain variable related to the thermal system, β h1 (t) represents the lower - bound dual variable of the uncertain variable fluctuation range related to the thermal system, β h2 (t) represents the upper - bound dual variable of the uncertain variable fluctuation range related to the electric system, C EC (t) represents the cold power generated by the electric chiller, C AC(t) represents the cooling power generated by the absorption chiller, C CS,d (t) represents the cooling power released by the cold energy storage device, C c,0 (t) represents the reference value of the cold load power fluctuation range, C CS,c (t) represents the cooling power charged into the cold energy storage device, α c (t) represents the introduced dual variable related to the cold system, Γ c (t) represents the introduced conservative parameter related to the uncertain variable of the cold system, β c1 (t) represents the introduced lower limit dual variable of the uncertain variable fluctuation range related to the cold system, β c2 (t) represents the introduced upper limit dual variable of the uncertain variable fluctuation range related to the cold system, G GS,d (t) represents the gas release power of the gas energy storage device, G L,G0 (t) represents the reference value of the gas load power fluctuation range, V FC (t) represents the natural gas consumption of the fuel cell, α g (t) represents the introduced dual variable related to the gas system, Γ g (t) represents the introduced conservative parameter related to the uncertain variable of the gas system, β g1 (t) represents the introduced lower limit dual variable of the uncertain variable fluctuation range related to the gas system, β g2 (t) The introduced upper limit dual variable of the uncertain variable fluctuation range related to the gas system.

[0066] In step 3, obtaining the initial state transition path of the multi - energy micro - grid day - ahead robust optimal scheduling includes the following steps:

[0067] S31: Simplify the operating state of the multi - energy micro - grid:

[0068] Define two 0 - 1 variables v d (t) and v q (t) to simplify the operating state of the multi - energy micro - grid. When v d (t) = 1, it means that at this time, the multi - energy micro - grid preferentially purchases electric energy from the power grid to meet the load demand. When v d (t) = 0, it means that at this time, the multi - energy micro - grid is not inclined to purchase electric energy from the power grid. When v q (t) = 1, it means that at this time, the multi - energy micro - grid preferentially purchases gas energy from the gas network to meet the load demand. When v q (t) = 0, it means that at this time, the multi - energy micro - grid is not inclined to purchase gas energy from the gas network. Then, according to the different values of the two variables, the operating state of the multi - energy micro - grid can be simplified into 4 types

[0069] S32: Determine the initial state transition path of the multi - energy micro - grid:

[0070] Taking the average of the electricity price and the natural gas price as the threshold, the variables v d (t) and v q (t) at the current time period are determined by comparing the electricity price and the natural gas price in each time period with the threshold. For example, when the electricity price and the natural gas price are higher than the threshold, the variables v d (t) and v q (t) take the value of 0. On the contrary, when the electricity price and the natural gas price are lower than the threshold, the variables v d (t) and v q (t) take the value of 1. Based on the above rules, the operating states of the multi - energy micro - grid at each moment can be obtained, that is, the initial state transition path of the multi - energy micro - grid is obtained; S33: Determine the operating states of each device in the initial state transition path of the multi - energy micro - grid:

[0071] For the energy conversion unit, since the multi - energy micro - grid needs to purchase energy from the power grid and the natural gas network to meet the load demand, the operating states of the gas turbine and the gas boiler can be determined as follows:

[0072]

[0073] In formula (22): s gt and s gb represent the operating states of the gas turbine and the gas boiler respectively; s gt and s gb taking the value of 1 indicates being in the on - load operation state, and conversely being in the shutdown state.

[0074] For the electric chiller and the absorption chiller, since the chiller mainly supplies the cooling load, the operating state of the chiller can be determined by equivalently converting the cooling load into the electric and thermal loads as follows:

[0075]

[0076] In formula (23): s ac and s ec are the operating states of the absorption chiller and the electric chiller respectively; v d represents the state of the multi - energy micro - grid purchasing electric energy from the power grid, and v q represents the state of the multi - energy micro - grid purchasing natural gas from the natural gas network.

[0077] For the energy storage unit, the operating state of the energy storage unit can be determined according to the system's tendency to purchase electricity and gas as follows:

[0078]

[0079] In formula (24): s bs 、s ts 、sgs and s cs respectively represent the operating states of the electricity storage unit, heat storage unit, gas storage unit, and cold storage unit.

[0080] S34: Determine the output of each device in the initial state transition path of the multi-energy microgrid:

[0081] Based on the established day-ahead robust optimal scheduling model of the multi-energy microgrid in formula (21), the output of each device in the initial state transition path of the multi-energy microgrid can be determined.

[0082] In step 4, the improvement of the improved genetic algorithm lies in: performing mutation operations on the obtained initial state transition path multiple times, a series of sub-paths with the initial sequence as the maternal line can be obtained, and these paths are used as the initialized population; in step 4, to obtain the optimal day-ahead robust state transition path of the multi-energy microgrid, an improved genetic algorithm is used to optimize the path;

[0083] First, perform mutation operations on the initial state transition path multiple times, a series of sub-paths with the initial sequence as the maternal line can be obtained, and these paths are used as the initialized population;

[0084] Assume that an initial state transition path S0 can be expressed as follows:

[0085] S0 = [s1, s2, s3,... s t ,..., s T (25);

[0086] In formula (25): s t represents the state of the multi-energy microgrid at time t, and T represents the scheduling period.

[0087] Perform a single-point mutation operation on the initial state transition path S0. Specifically, for a given path S, the steps to generate a mutated path S' are as follows:

[0088] 1) Randomly select a time t, 1 ≤ t ≤ T;

[0089] 2) Randomly generate a new state s t ';

[0090] 3) Replace s t in S with s t ', to obtain S', as shown below.

[0091] S' = [s1, s2,... s t-1 , s t ', s t+1 ,..., s T (26);

[0092] Starting from the initial path S0, the mutation operation is repeated N times to generate N sub-paths S1, S2, …, S N , and these sub-paths together with S0 form the initial population ρ, as shown below.

[0093] ρ = {S0, S1, S2, …, S N}(27);

[0094] In formula (27): S i is obtained by mutating S0 or a certain S j (j < i).

[0095] Secondly, calculate the population fitness. Taking the operating cost as the fitness, select and eliminate in the way of roulette, and optimize the path through a series of operations such as crossover and mutation succession, as shown in formula (28) below:

[0096]

[0097] In formula (28): f i is the total cost of the i-th individual, F is the sum of the total costs of all individuals, P i represents the probability that an individual is selected and eliminated; N represents the number of individuals.

[0098] Then, retain some of the best individuals, which can prevent the phenomenon of negative optimization and reduce meaningless iterations; the steps of retaining some of the best individuals are as follows:

[0099] 1) Select M parental individuals according to the probability P i , M < N;

[0100] 2) Perform crossover and mutation on the selected M parental individuals to generate N - K offspring individuals, where K represents the number of the selected best individuals.

[0101] 3) Directly retain K optimal individuals ε t from the current population ρ t , and merge them with the generated N - K offspring individuals to form a new generation population, as shown in formula (29).

[0102] ρ t+1 = ε t ∪ C t (29);

[0103] In the formula: C t is the set of offspring individuals generated by crossover and mutation, and the selection method of selecting K optimal individuals ε t from the population ρ t is to select the K individuals with the largest sum of fitness values, as follows:

[0104]

[0105] In the formula: f represents the total cost of an individual, f i represents the total cost of the i-th individual.

[0106] Finally, design the threshold and the upper limit of the number of iterations. When the optimization reaches the threshold or the limit of the number of iterations, the iteration ends, and the obtained result can be regarded as the optimal solution. As shown below.

[0107]

[0108] In the formula: l and l max respectively represent the number of iterations and the maximum number of iterations, f th represents the optimal individual fitness threshold; True means that the judgment condition of the termination condition is true, and False means that the judgment condition of the termination condition is false.

[0109] The present invention relates to a day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling, and the technical effects are as follows:

[0110] 1) In step 1 of the present invention, by establishing a state transition model of the multi-energy microgrid, the operation states of the multi-energy microgrid at different stages can be clearly presented, improving the observability of the optimal regulation and control of the multi-energy microgrid.

[0111] 2) In step 2 of the present invention, by constructing an uncertainty set, the double fluctuations of the wind-solar power output and the load demand can be effectively dealt with, significantly improving the reliability of the operation of the multi-energy microgrid; the robust optimization method can ensure the feasible solution of the multi-energy microgrid in the worst scenario, avoiding the over-limit risk that may occur in traditional deterministic optimization.

[0112] 3) In step 3 of the present invention, by simplifying the state of the multi-energy microgrid, setting two variables and obtaining the initial state transition path of the multi-energy microgrid based on historical data can significantly accelerate the algorithm convergence and reduce the number of iterations; secondly, by covering a diverse feasible solution space, the quality of the solution can be effectively improved and the local optimum can be avoided.

[0113] 4) In step 4 of the present invention, by repeatedly mutating the initial state transition path multiple times, a series of sub-paths with the initial sequence as the maternal line can be obtained, and these paths are used as the initial population. Since the obtained initial state transition path is already very good, the computational workload of the genetic algorithm for solving the optimal state transition path will be greatly reduced, effectively improving the problems of large consumption of computing resources and long computing time of the traditional genetic algorithm.

[0114] 5) The state transition model of the multi-energy microgrid established by the present invention realizes the visual description of the system operation state at different operation stages; it can not only ensure the optimality of the solution but also has a low solution time complexity. Description of the Drawings

[0115] The present invention will be further described below in conjunction with the drawings and embodiments:

[0116] Figure 1 It is a flowchart of the method of the present invention.

[0117] Figure 2 It is a structural diagram of a multi - energy micro - grid.

[0118] Figure 3 It is a diagram of a hybrid state machine.

[0119] Figure 4 It is a state - transfer path diagram of a multi - energy micro - grid.

[0120] Figure 5 It is a waveform diagram of the operating state of a multi - energy micro - grid and its state - transfer process. Detailed Embodiment

[0121] As Figure 1 shown, for the day - ahead optimal scheduling method of a multi - energy micro - grid based on hybrid state - machine modeling, the specific steps are as follows:

[0122] Step 1: Establish a state - transfer model of the multi - energy micro - grid to describe the dynamic characteristics of the multi - energy micro - grid in different states;

[0123] Figure 2 The structural diagram of the multi - energy micro - grid consists of various energy input, conversion, and energy - storage devices. The multi - energy micro - grid supplies energy to the load through the coordinated complementarity among various energy units.

[0124] The process of establishing the state - transfer model of the multi - energy micro - grid is as follows:

[0125] 1) Divide the multi - energy micro - grid into multiple optimizable adjustment states, and each optimizable adjustment state consists of the operating states of the schedulable units in the multi - energy micro - grid;

[0126] 2) Select the energy - storage states of electricity storage, gas storage, and heat storage in the multi - energy micro - grid as state variables, and select the purchased and sold electric power and gas purchase volume as control variables, and establish the state - space equation of the multi - energy micro - grid as shown below;

[0127]

[0128] In the formula: A, B, D, and E respectively represent the state matrix, control matrix, coupling matrix, and perturbation matrix of the multi - energy micro - grid; x(t), u(t), i(t), and d(t) respectively represent the state variables, control variables, coupling variables, and perturbation variables of the multi - energy micro - grid;

[0129] Among them, each variable is represented as follows.

[0130]

[0131] Where: SOC(t), SOG(t), and SOH(t) respectively represent the stored energy of the electricity storage, gas storage, and heat storage units, η bs , η gs and η ts respectively represent the energy conversion efficiency coefficients of the electricity storage, gas storage, and heat storage units; v b (k) and P gb (t) respectively represent the power purchase status and power purchase power of the multi - energy micro - grid, v s (t) and P gs (t) respectively represent the power sale status and power sale power of the multi - energy micro - grid; P sb (t) represents the gas purchase power of the multi - energy micro - grid; V MT (t) represents the amount of natural gas consumed by the gas turbine, P P2G (t) represents the electric power consumed by the power - to - gas equipment, V FC (t) represents the amount of natural gas consumed by the fuel cell, V GB (t) represents the amount of natural gas consumed by the gas boiler, P EC (t) represents the electricity consumed by the electric chiller, Q AC (t) represents the heat absorbed by the absorption chiller. P PV (t), P WT (t), P L,E (t), G L,G (t) and Q L,H (t) respectively represent the photovoltaic power generation, wind power output, electrical load power, gas load, and heat load.

[0132] Among them, the coefficient matrix is expressed as follows.

[0133]

[0134]

[0135] Where: η gt , η loss and H ng respectively represent the electrical conversion efficiency, heat loss rate, and heat conversion efficiency of the gas turbine; η FC , η GB and μ P2G respectively represent the conversion efficiencies of the fuel cell, gas boiler, and power - to - gas equipment; L HV represents the lower calorific value of natural gas.

[0136] 3): Establish a state transition model of the multi - energy micro - grid based on the hybrid state machine.

[0137] Figure 3 It is a diagram of a hybrid state machine. A circle represents an optimizable adjustment state of the multi - energy micro - grid at present, and the state - space equation inside the circle describes the dynamic behavior characteristics of the multi - energy micro - grid in the current operating state; an arrow represents the transfer process of the multi - energy micro - grid state, and the conditions for the transfer of the multi - energy micro - grid between different operating states are marked on the arrow. The conditions can be based on the operating state of the energy unit and external factors (such as electricity price, gas price fluctuations, load demand changes).

[0138] Step 2: Establish a day - ahead robust optimal scheduling model for the multi - energy micro - grid considering load and renewable energy output fluctuations;

[0139] In the day - ahead robust optimal scheduling model of the multi - energy micro - grid, the optimization objective F includes the operating cost F1, the system carbon emission cost F2, and the load power outage rate F3, as follows:

[0140] F = min[F1 + F2 + F3] (9);

[0141] Among them:

[0142]

[0143] In the formula: c e (t) and c s (t) represent the electricity purchase price and the electricity selling price respectively, c g (t) represents the gas purchase price, M represents the number of devices in the multi - energy micro - grid, k o,m represents the unit operation and maintenance cost of device m, P m (t) represents the output of device m at time t. θ represents the treatment cost per unit of CO2 emission, γ e , γ g and γ gt are the equivalent carbon emission coefficients for electricity purchase, gas purchase, and gas turbine operation of the multi - energy micro - grid respectively, P gt (t) is the output of the gas turbine at time t. P load is the load power of the multi - energy micro - grid, P loss is the missing load power of the multi - energy micro - grid.

[0144] The constraint conditions in the day - ahead robust optimal scheduling model of the multi - energy micro - grid include power balance constraints, device unit operation constraints, energy storage unit operation constraints, and energy purchase power constraints.

[0145] ① Power balance constraints

[0146] Electric power balance:

[0147] P PV (t)+P WT (t)+P gt (t)+Pgb (t) + P FC (t) + P BS,d (t) = P EC (t) + P gs (t) + P BS,c (t) + P L,E (t) + P P2G (t)(11);

[0148] In the formula, P on the left side of the equation FC (t) and P BS,d (t) respectively represent the output power of the fuel cell and the discharge power of the electricity storage unit at time t. P on the right side of the equation BS,c (t) and P P2G (t) respectively represent the charging power of the electricity storage unit and the electric power consumed by the power - to - gas device at time t.

[0149] Thermal power balance:

[0150] Q gb (t) + Q hb (t) + Q TS,d (t) = Q TS,c (t) + Q AC (t) + Q h (t)(12);

[0151] In the formula: Q on the left side of the equation gb (t), Q hb (t) and Q TS,d (t) respectively represent the heat output of the gas boiler, the heat output of the waste heat recovery boiler and the heat released by the heat storage unit at time t. Q on the right side of the equation TS,c (t) and Q AC (t) respectively represent the heat absorbed by the heat storage unit and the heat consumed by the absorption chiller at time t.

[0152] Cooling power balance:

[0153] C EC (t) + C AC (t) + C CS,d (t) = C c (t) + C CS,c (t)(13);

[0154] In the formula, C on the left side of the equation EC (t), C AC (t) and C CS,d (t) respectively represent the cooling capacity of the electric chiller, the cooling capacity of the absorption chiller and the cold released by the cold storage device at time t. C on the right side of the equation CS,c (t) and C c(t) represents the cooling capacity absorbed by the cold energy storage device and the cooling load power at time t.

[0155] Gas power balance:

[0156] G gas (t) + G P2G (t) + G GS,d (t) = G L,G (t) + V FC (t) + V MT (t) + V GB (t) + G GS,c (t)(14);

[0157] In the formula, on the left side of the equation, G P2G (t) and G GS,d (t) respectively represent the gas power output by the power-to-gas equipment and the natural gas volume released by the gas storage device at time t. On the right side of the equation, G GS,c (t) represents the natural gas volume stored in the gas storage device at time t.

[0158] ② Equipment unit operation constraints

[0159]

[0160] In the formula: η rec represents the recovery efficiency of the waste heat boiler, k ec and k ac respectively represent the refrigeration efficiencies of the electric chiller and the absorption chiller.

[0161] ③ Energy storage unit operation constraints

[0162]

[0163] In the formula, x is the type of energy storage, and are the energy states before and after energy charging and discharging respectively, η x,c and η x,d respectively represent the charging efficiency and discharging efficiency, and are the charging and discharging power at time t respectively.

[0164]

[0165] In the formula, E x,max and E x,min are the upper and lower limit values of the energy storage device respectively, P x,c,max and P x,d,max are the maximum values of the charging and discharging energy respectively, v x,c and v x,d are the 0-1 state variables of the charging and discharging energy respectively.

[0166] ④ Purchase energy power constraint

[0167]

[0168] In the formula, P gb,max and P gs,max respectively represent the maximum power of purchasing electricity and selling electricity, and G gas,max represents the maximum power of purchasing gas.

[0169] Considering that the prediction errors of the renewable energy and load power are relatively large in the day-ahead optimal scheduling stage, the following equations are used to describe the prediction deviations of the renewable energy and load power, as shown below.

[0170]

[0171] In the formula: β and respectively represent the actual value and predicted value of the renewable energy and load power, and △β is the fluctuation range of β; β ud and β ld are respectively the upper and lower limits of △β, and ξ ud and ξ ld are respectively the proportional deviations of β relative to △β.

[0172] Then, a day-ahead robust optimal scheduling model of the multi-energy microgrid considering the fluctuations of renewable energy and load output can be established, as shown below.

[0173]

[0174] In the formula: x and β are decision variables and uncertain variables respectively, and X and W are their corresponding sets. To solve this model, the dual variables α i , β i1 , β i2 and Γ i are introduced to transform the above model (17) into the solution of a deterministic optimization problem under the most adverse uncertain scenario, as shown below.

[0175]

[0176] Step 3: Obtain the initial state transition path of the day-ahead robust optimal scheduling of the multi-energy microgrid, including the following steps;

[0177] 1) Simplify the operating state of the multi-energy microgrid

[0178] Define two 0-1 variables v d (t) and v q to simplify the operating state of the multi-energy microgrid. When v d (t) = 1, it means that at this time, the multi-energy microgrid preferentially purchases electric energy from the power grid to meet the load demand. When v dWhen t = 0, it means that the multi - energy micro - grid does not tend to purchase electric energy from the power grid at this time. When v q When t = 1, it means that the multi - energy micro - grid preferentially purchases gas energy from the gas network to meet the load demand. When v q When t = 0, it means that the multi - energy micro - grid does not tend to purchase gas energy from the gas network. Then, according to the different values of the two variables, the operating states of the multi - energy micro - grid can be simplified into 4 types.

[0179] 2) Determine the initial state - transfer path of the multi - energy micro - grid.

[0180] Figure 4 Taking the average value of the electricity price and the natural gas price as the threshold, the state - transfer path diagram of the multi - energy micro - grid is obtained. By comparing the electricity price and the natural gas price in each period with the threshold, the values of the variables v d (t) and v q (t) are determined. For example, when the electricity price and the natural gas price are higher than the threshold, the variables v d (t) and v q (t) take the value of 0. On the contrary, when the electricity price and the natural gas price are lower than the threshold, the variables v d (t) and v q (t) take the value of 1. Based on the above rules, the operating states of the multi - energy micro - grid at each moment can be obtained, that is, the initial state - transfer path of the multi - energy micro - grid is obtained.

[0181] 3) Determine the operating states of each device in the initial state - transfer path of the multi - energy micro - grid.

[0182] For the energy conversion unit, since the multi - energy micro - grid needs to purchase energy from the power grid and the gas network to meet the load demand, the operating states of the gas turbine and the gas boiler can be determined as follows:

[0183]

[0184] In the formula: s gt and s gb represent the operating states of the gas turbine and the gas boiler respectively. The value of s gt and s gb taking 1 means being in the on - load operation state, and vice versa being in the shutdown state.

[0185] For the electric chiller and the absorption chiller, since the chiller mainly supplies the cooling load, the operating states of the chiller can be determined by equivalent the cooling load to the electric and heat loads as follows:

[0186]

[0187] In the formula: s ac and s ecThey are the operating states of an absorption chiller and an electric chiller respectively.

[0188] For the energy storage unit, its operating state can be determined according to the system's electricity and gas purchase tendencies as follows:

[0189]

[0190] In the formula: s bs , s ts , s gs and s cs represent the operating states of the electricity storage unit, the heat storage unit, the gas storage unit, and the cold storage unit respectively.

[0191] 4) Determine the output of each device in the initial state transition path of the multi - energy micro - grid

[0192] Based on the established day - ahead robust optimal dispatch model (21) of the multi - energy micro - grid, the output of each device in the initial state transition path of the multi - energy micro - grid can be determined.

[0193] Step 4: Obtain the optimal state transition path of the multi - energy micro - grid day - ahead robust optimal dispatch based on the improved genetic algorithm;

[0194] To obtain the optimal day - ahead robust state transition path of the multi - energy micro - grid, the improved genetic algorithm is used to optimize the path. First, the mutation operation is repeated multiple times on the initial state transition path to obtain a series of sub - paths with the initial sequence as the maternal line, and these paths are used as the initial population; secondly, the population fitness is calculated, with the operating cost as the fitness, and selection and elimination are carried out in the way of roulette, and a series of operations such as crossover and mutation succession cycles are used to optimize the path, as shown in formula (22) below. Then, part of the most excellent individuals are retained to prevent the phenomenon of negative optimization and reduce meaningless iterations; finally, the threshold and the upper limit of the number of iterations are designed, and when the optimization reaches the threshold or the iteration limit, the iteration ends, and the obtained result can be regarded as the optimal solution.

[0195]

[0196] In the above formula, f i is the total cost of the i - th individual, F is the sum of the total costs of all individuals, and P i represents the probability that the individual is selected and eliminated.

[0197] Verification example:

[0198] To verify the feasibility of the proposed modeling method of the present invention, the proposed modeling method (denoted as Method 1) is compared with the exhaustive solution algorithm (denoted as Method 2) and the traditional energy hub-based modeling method (denoted as Method 3), and the daily operating cost and solution time of the multi-energy microgrid under the three methods are compared. The obtained results are shown in Table 1 below.

[0199] Table 1 Operating costs under the three methods

[0200]

[0201] As can be seen from Table 1, the solution time of Method 3 is the smallest. This is mainly because Method 3 obtains the initial state transition path of the multi-energy microgrid, so the solution speed is relatively fast. However, it cannot guarantee the optimality of the solution, so its cost is the highest. Method 2 can ensure finding the optimal solution to the problem, but when the operating states of the multi-energy microgrid increase, the solution time of Method 2 will increase significantly. Under Method 1, since the established state transition model can simplify the representation of system states and the state transition process, it can reduce the computational complexity of optimal scheduling to a certain extent and improve the solution efficiency. Therefore, the solution time of Method 1 is less than that of Method 2. Comparing the operating costs of Method 1 and Method 2 shows that their operating costs are close, indicating that Method 1 can guarantee the optimality of the solution.

[0202] Figure 5 Indicating the operating state of the multi-energy microgrid and its state transition process diagram, it can be seen that the proposed modeling method of the present invention realizes the full-process observability of the operating state of the multi-energy microgrid and the state trajectories of each energy unit in the multi-energy microgrid, manifested in that at any time, through Figure 5 it is possible to know the current operating state of the multi-energy microgrid and the operating states of each energy unit.

[0203] In summary, the proposed modeling method of the present invention can not only guarantee the optimality of the solution but also has a low solution time complexity.

Claims

1. A day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling, characterized in that It includes the following steps: Step 1: Establish a state transition model of the multi - energy micro - grid to describe the dynamic characteristics of the multi - energy micro - grid in different states; Step 2: Establish a day - ahead robust optimal scheduling model of the multi - energy micro - grid considering the fluctuations of load and renewable energy output; Step 3: Obtain the initial state transition path of the day - ahead robust optimal scheduling of the multi - energy micro - grid; Step 4: Based on the improved genetic algorithm, obtain the optimal state transition path of the day - ahead robust optimal scheduling of the multi - energy micro - grid.

2. The day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling according to claim 1, characterized in that: In the said Step 1, establishing the state transition model of the multi - energy micro - grid includes the following steps: S1.1: Divide the multi - energy micro - grid into multiple optimizable adjustment states, and each optimizable adjustment state consists of the operating states of the dispatchable units in the multi - energy micro - grid; S1.2: Select the energy storage states of electricity storage, gas storage and heat storage in the multi - energy micro - grid as state variables, select the purchased and sold electric power and gas purchase volume as control variables, and establish the state - space equation of the multi - energy micro - grid as follows: In Equation (1): A, B, D and E respectively represent the state matrix, control matrix, coupling matrix and perturbation matrix of the multi - energy micro - grid; x(t), u(t), i(t) and d(t) respectively represent the state variable, control variable, coupling variable and perturbation variable of the multi - energy micro - grid; x(t + 1) represents the state variable at time t + 1; S1.3: Based on the hybrid state machine, establish the state transition model of the multi - energy micro - grid.

3. The day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling according to claim 2, characterized in that: In S1.2, each variable is represented as follows: In Equation (2): SOC(t), SOG(t) and SOH(t) respectively represent the stored energy of the electricity storage, gas storage and heat storage units; η bs , η gs and η ts respectively represent the energy conversion efficiency coefficients of the electricity storage, gas storage, and heat storage units; v b (t) and P gb (t) respectively represent the electricity purchase status and electricity purchase power of the multi - energy micro - grid, v s (t) and P gs (t) respectively represent the electricity sale status and electricity sale power of the multi - energy micro - grid; P sb (t) represents the gas purchase power of the multi - energy micro - grid; V MT (t) represents the amount of natural gas consumed by the gas turbine, P P2G (t) represents the electric power consumed by the power - to - gas equipment, V FC (t) represents the amount of natural gas consumed by the fuel cell, V GB (t) represents the amount of natural gas consumed by the gas boiler, P EC (t) represents the electricity consumed by the electric chiller, Q AC (t) represents the heat absorbed by the absorption chiller; P PV (t), P WT (t), P L,E (t), G L,G (t) and Q L,H (t) respectively represent the photovoltaic power generation, wind power output, electrical load power, gas load, and heat load; T represents the symbol for matrix transpose operation.

4. The day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling according to claim 2, characterized in that: In S1.2, the coefficient matrix is represented as follows: In the above formula: η gt , η loss and H ng respectively represent the electrical conversion efficiency, heat loss rate and heat conversion efficiency of the gas turbine; η FC ,η GB and represent the conversion efficiencies of the fuel cell, gas boiler, and power-to-gas equipment, respectively; L HV represents the lower calorific value of natural gas.

5. The day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling according to claim 2, characterized in that: In S1.3, based on the hybrid state machine, establish the state transition model of the multi - energy micro - grid, where the expression of the hybrid state machine is as follows; H = {S, X, F, G} (6); In the formula: S represents the set of discrete states, X represents the set of continuous state variables, F represents the continuous dynamic equation function, and G represents the state transition condition; For the multi - energy micro - grid, the set of discrete states is as follows; S = {s1, s2, s3, … s n} (7); where: s i represents an operating state of the multi - energy micro - grid; for the continuous state variable X in the multi - energy micro - grid, the stored energies of the electricity storage, gas storage, and heat storage units are selected as the continuous state variables, as shown in Equation (2); regarding the continuous dynamic equation function F, see the state - space equation (1) of the established multi - energy micro - grid; then the state - transfer model of the multi - energy micro - grid can be established as follows: The above formula represents the state - space equation of the multi - energy micro - grid in different operating states; the conversion between different states is triggered by the state transition condition G.

6. The day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling according to claim 1, characterized in that: In the said Step 2, in the day - ahead robust optimal scheduling model of the multi - energy micro - grid, the optimization objective F includes the operating cost F1, the system carbon emission cost F2 and the load power shortage rate F3, as follows: F = min[F1 + F2 + F3] (9); Among them: In formula (10): c e (t) and c s (t) represent the electricity purchase price and the electricity selling price respectively; c g (t) represents the gas purchase price, and M represents the number of devices in the multi - energy micro - grid; k o,m represents the unit operation and maintenance cost of device m; P m (t) represents the output of device m at time t; θ represents the treatment cost per unit of CO2 emissions; γ e , γ g and γ gt are the equivalent carbon emission coefficients for electricity purchase, gas purchase and gas turbine operation in the multi - energy micro - grid respectively; P gt (t) is the output of the gas turbine at time t; P load is the load power of the multi - energy micro - grid; P loss is the missing load power of the multi - energy micro - grid; △t represents the scheduling time interval.

7. The day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling according to claim 6, characterized in that: The constraint conditions in the day - ahead robust optimal scheduling model of the multi - energy micro - grid include power balance constraints, equipment unit operation constraints, energy storage unit operation constraints, and energy purchase power constraints; ①. Power balance constraints: a: Electric power balance: P PV (t) + P WT (t) + P gt (t) + P gb (t) + P FC (t) + P BS,d (t) = P EC (t) + P gs (t) + P BS,c (t) + P L,E (t) + P P2G (t) (11); In formula (11): P on the left side of the equation FC (t) and P BS,d (t) respectively represent the output power of the fuel cell and the discharge power of the electricity storage unit at time t; P on the right side of the equation BS,c (t) and P P2G (t) respectively represent the charging power of the electricity storage unit and the electric power consumed by the power-to-gas device at time t; P PV (t) represents the photovoltaic output power, P WT (t) represents the wind power output, P gt (t) represents the output power of the gas turbine, P gb (t) represents the purchased power, P EC (t) represents the rated electric power consumed by the electric chiller, P gs (t) represents the sold power, P L,E (t) represents the electric load power; b: Thermal power balance: Q gb (t) + Q hb (t) + Q TS,d (t) = Q TS,c (t) + Q AC (t) + Q h (t) (12); In Equation (12): On the left side of the equation, Q gb (t), Q hb (t), and Q TS,d (t) respectively represent the heat production of the gas boiler, the heat production of the waste heat recovery boiler, and the heat released by the heat storage unit at time t; on the right side of the equation, Q TS,c (t) and Q AC (t) respectively represent the heat absorbed by the heat storage unit and the heat consumed by the absorption chiller at time t; Q h (t) represents the heat load power; c: Cooling power balance: C EC (t) + C AC (t) + C CS,d (t) = C c (t) + C CS,c (t)(13); In formula (13): On the left side of the equation, C EC (t), C AC (t) and C CS,d (t) respectively represent the refrigerating capacity of the electric refrigerator, the refrigerating capacity of the absorption refrigerator, and the cold quantity released by the cold storage device at time t; on the right side of the equation, C CS,c (t) and C c (t) represent the cold quantity absorbed by the cold storage device and the cold load power at time t; d: Gas power balance: G gas (t)+G P2G (t)+G GS,d (t)=G L,G (t)+V FC (t)+V MT (t)+V GB (t)+G GS,c (t) (14); In formula (14): On the left side of the equation, G P2G (t) and G GS,d (t) respectively represent the gas power output by the power-to-gas device and the amount of natural gas released from the gas storage device at time t; on the right side of the equation, G GS,c (t) represents the amount of natural gas stored in the gas storage device at time t; G gas (t) represents the gas purchase power, G L,G (t) represents the gas load power, V FC (t) represents the amount of natural gas consumed by the fuel cell, V MT (t) represents the amount of natural gas consumed by the gas turbine, V GB (t) represents the amount of natural gas consumed by the gas boiler; ②. Equipment unit operation constraints: In formula (15): η rec represents the recovery efficiency of the waste heat boiler, k ec and k ac respectively represent the refrigeration efficiencies of the electric chiller and the absorption chiller; ③. Energy storage unit operation constraints: In formula (16): x is the type of energy storage; and are the energy states before and after energy charging and discharging, respectively; η x,c and η x,d represent the charging efficiency and discharging efficiency respectively; and are the charging and discharging powers at time t respectively; bs, ts, cs, gs respectively represent the electric energy storage, thermal energy storage, cooling energy storage and gas energy storage devices; In formula (17): E x,max and E x,min are the upper and lower limit values of the energy storage device respectively, P x,c,max and P x,d,max are the maximum values of the charging and discharging energies respectively, v x,c and v x,d are the 0-1 state variables of the charging and discharging energies respectively; represents the stored energy of the energy storage device at the initial moment of the scheduling period, represents the stored energy of the energy storage device at the end moment of the scheduling period; ④. Energy purchase power constraints: In formula (18): P gb,max and P gs,max respectively represent the maximum power of electricity purchase and electricity sale, and G gas,max represents the maximum power of gas purchase.

8. The day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling according to claim 7, characterized in that: Considering that the prediction errors of the renewable energy and load power are relatively large in the day - ahead optimal scheduling stage, the following equations are used to describe the prediction deviations of the renewable energy and load power, as follows: In formula (19): β and respectively represent the actual and predicted values of renewable energy and load power; △β is the fluctuation range of β; β ud and β ld are respectively the upper and lower limits of △β; ξ ud and ξ ld are respectively the proportional deviations of β with respect to △β; A day-ahead robust optimal scheduling model for a multi-energy microgrid considering the fluctuations of renewable energy and load output is established as follows: In Equation (20): x and β are decision variables and uncertain variables, respectively; X and W are their corresponding sets; in order to solve this model, the dual variables α i , β i1 , β i2 and Γ i ; The above model formula (20) is transformed into the solution of a deterministic optimization problem under the worst-case uncertain scenario as follows: In Equation (21): x ∈ {e, h, c, g} represents decision variables, where e, h, c, and g respectively represent the symbols for electricity, heat, cold, and gas. represents the conservative parameter of the uncertain variables related to electricity, heat, cold, and gas; P PV,0 (t) represents the reference value of the photovoltaic output power fluctuation range, P WT,0 (t) represents the reference value of the wind power output fluctuation range, P L,E0 represents the reference value of the electric load power fluctuation range, α e (t) represents the introduced dual variable related to the electric system, Γ e (t) represents the conservative parameter introduced related to the uncertain variables of the electric system, β e1 (t) represents the lower bound dual variable of the introduced uncertain variable fluctuation range related to the electric system, β e2 (t) represents the upper bound dual variable of the introduced uncertain variable fluctuation range related to the electric system, β pv1 (t) represents the dual variable of the lower limit of the photovoltaic output power fluctuation range, β pv2 (t) represents the dual variable of the upper limit of the photovoltaic output power fluctuation range, β wt1 (t) represents the dual variable of the lower limit of the wind power output fluctuation range, β wt2 (t) represents the dual variable of the upper limit of the wind power output fluctuation range, Q TS,d (t) represents the heat release power of the thermal energy storage device, Q TS,c (t) represents the heat charging power of the thermal energy storage device, Q ac (t) represents the heat power consumed by the absorption chiller, Q h,0 (t) represents the reference value of the heat load power fluctuation range, α h (t) represents the introduced dual variable related to the thermal system, Γ h (t) represents the conservative parameter introduced related to the uncertain variables of the thermal system, β h1 (t) represents the lower bound dual variable of the introduced uncertain variable fluctuation range related to the thermal system, β h2 (t) represents the upper bound dual variable of the introduced uncertain variable fluctuation range related to the electric system, C EC (t) represents the cold power generated by the electric chiller, C AC (t) represents the cold power generated by the absorption chiller, C CS,d (t) represents the cold release power of the cold energy storage device, C c,0 (t) represents the reference value of the cold load power fluctuation range, C CS,c (t) represents the cold charging power of the cold energy storage device, α c (t) represents the introduced dual variable related to the cold system, Γ c (t) represents the introduced conservative parameter related to the uncertain variables of the cold system, β c1 (t) represents the introduced dual variable of the lower limit of the fluctuation range of the uncertain variables related to the cold system, β c2 (t) represents the introduced dual variable of the upper limit of the fluctuation range of the uncertain variables related to the cold system, G GS,d (t) represents the gas discharge power of the gas energy storage device, G L,G0 (t) represents the reference value of the fluctuation range of the gas load power, V FC (t) represents the natural gas consumption of the fuel cell, α g (t) represents the introduced dual variable related to the gas system, Γ g (t) represents the introduced conservative parameter related to the uncertain variables of the gas system, β g1 (t) represents the introduced dual variable of the lower limit of the fluctuation range of the uncertain variables related to the gas system, β g2 (t) The introduced dual variable of the upper limit of the fluctuation range of the uncertain variables related to the gas system.

9. The day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling according to claim 1, characterized in that: In step 3, obtaining the initial state transition path of the day-ahead robust optimal scheduling of the multi-energy microgrid includes the following steps: S31: Simplify the operating state of the multi-energy microgrid: Define two 0-1 variables v d (t) and v q (t) to simplify the operating states of the multi-energy microgrid; when v d (t) = 1, it means that at this time the multi-energy microgrid preferentially purchases electric energy from the power grid to meet the load demand. When v d (t) = 0, it means that at this time the multi-energy microgrid is not inclined to purchase electric energy from the power grid; when v q (t) = 1, it means that at this time the multi-energy microgrid preferentially purchases gas energy from the gas grid to meet the load demand. When v q (t) = 0, it means that at this time the multi-energy microgrid is not inclined to purchase gas energy from the gas grid; then according to the different values of the two variables, the operating states of the multi-energy microgrid can be simplified into four S32: Determine the initial state transition path of the multi-energy microgrid: Taking the average of the electricity price and the natural gas price as the threshold, the values of the current period variables v d (t) and v q (t) are determined by comparing the electricity price and the natural gas price of each period with the threshold; for example, when the electricity price and the natural gas price are higher than the threshold, the variables v d (t) and v q (t) take the value of 0, and conversely, when the electricity price and the natural gas price are lower than the threshold, the variables v d (t) and v q (t) take the value of 1; based on the above rules, the operating state of the multi - energy micro - grid at each moment can be obtained, that is, the initial state transition path of the multi - energy micro - grid is obtained; S33: Determine the operating states of the devices in the initial state transition path of the multi-energy microgrid: For the energy conversion unit, since the multi-energy microgrid needs to purchase energy from the power grid and the natural gas network to meet the load demand, the operating states of the gas turbine and the gas boiler can be determined as follows: In formula (22): s gt and s gb respectively represent the operating states of the gas turbine and the gas boiler; s gt and s gb Taking the value of 1 indicates being in the on-load operation state, otherwise being in the shutdown state; For the electric chiller and the absorption chiller, since the chiller mainly supplies the cooling load, the operating state of the chiller can be determined by equivalently converting the cooling load into electric and thermal loads as follows: In formula (23): s ac and s ec are the operating states of the absorption chiller and the electric chiller respectively; v d represents the state of the multi - energy micro - grid purchasing electric energy from the power grid, and v q represents the state of the multi - energy micro - grid purchasing natural gas from the natural gas network; For the energy storage unit, the operating state of the energy storage unit is determined according to the system's power and gas purchase tendencies as follows: In formula (24): s bs 、s ts 、s gs and s cs respectively represent the operating states of the electricity storage unit, the heat storage unit, the gas storage unit, and the cold storage unit; S34: Determine the output of the devices in the initial state transition path of the multi-energy microgrid: Based on the established day-ahead robust optimal scheduling model formula (21) of the multi-energy microgrid, the output of the devices in the initial state transition path of the multi-energy microgrid can be determined.

10. The day-ahead optimal scheduling method for a multi-energy microgrid based on hybrid state machine modeling according to claim 1, characterized in that: In step 4, to obtain the optimal day-ahead robust state transition path of the multi-energy microgrid, an improved genetic algorithm is used to optimize the path; First, the mutation operation is repeated multiple times on the initial state transition path to obtain a series of sub-paths with the initial sequence as the maternal line, and these paths are used as the initial population; Assume that an initial state transition path S0 can be expressed as follows: S0 = [s1, s2, s3, … s t , …, s T (25); In formula (25): s t represents the state of the multi-energy microgrid at time t, and T represents the scheduling period; The single-point mutation operation is performed on the initial state transition path S0. Specifically, for a given path S, the steps to generate a mutated path S' are as follows: 1) Randomly select a time t, 1 ≤ t ≤ T; 2) Randomly generate a new state s t '; 3) Replace s in S with s t ' to obtain S', as follows; t ' S' = [s1, s2, … s t-1 , s t ', s t+1 , …, s T (26); Starting from the initial path S0, perform the mutation operation N times repeatedly to generate N sub-paths S1, S2, …, S N , and these sub-paths together with S0 form the initial population ρ, as follows; ρ = {S0, S1, S2, …, S N}(27); In formula (27): S i is obtained by mutating S0 or a certain S j (j < i); Secondly, calculate the population fitness, use the operating cost as the fitness, select and eliminate by roulette wheel, and optimize the path through a series of operations such as crossover and mutation succession, as shown in formula (28) below: In formula (28): f i is the total cost of the i-th individual, F is the sum of the total costs of all individuals, P i represents the probability that an individual is selected for elimination; N represents the number of individuals; Then, retain some of the most excellent individuals to prevent the phenomenon of negative optimization and reduce meaningless iterations; the steps to retain some of the most excellent individuals are as follows: 1) Select M parent individuals according to the probability P i , where M < N; 2) Perform crossover and mutation on the selected M parent individuals to generate N - K offspring individuals, where K represents the number of selected most excellent individuals; 3) Directly retain K optimal individuals ε t from the current population ρ t , and merge them with the generated N - K offspring individuals to form a new generation of population, as shown in Equation (29); ρ t+1 = ε t ∪ C t (29); where: C t is the set of offspring individuals generated by crossover and mutation, where K optimal individuals ε t are selected from the population ρ t using the method of selecting the K individuals with the largest total fitness, as follows: where: f represents the total cost of an individual, f i represents the total cost of the i-th individual; Finally, design a threshold and an upper limit for the number of iterations. When the optimization reaches the threshold or the iteration limit, the iteration ends, and the obtained result can be regarded as the optimal solution; as follows: Where: l and l max represent the number of iterations and the maximum number of iterations respectively, and f th represents the optimal individual fitness threshold; True indicates that the decision condition for the termination condition is true, and False indicates that the decision condition for the termination condition is false.

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